Wu Xiao-Lin, VanRaden Paul M, Cole John, Norman H Duane
Council on Dairy Cattle Breeding, Bowie, MD 20716.
Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI 53706.
JDS Commun. 2024 Dec 12;6(2):231-236. doi: 10.3168/jdsc.2024-0622. eCollection 2025 Mar.
Best prediction (BP) has been used in the United States to estimate unobserved daily and lactation yields from known test-day yields since 1999. This method has proven more accurate than its predecessors. However, it has 2 remarkable challenges in practice. First, BP reduces the variance of estimated yields compared with actual yields. Reduced phenotypic variance represents a concern because it can significantly underestimate genetic variations in genetic evaluations. Second, measurement errors occur in the projected lactation yields from incomplete or inaccurate test-day records. These errors can adversely affect the accuracy of lactation yield estimations and the subsequent genetic evaluations. This article provides an analytical review of BP, focusing on variance reduction and measurement errors. We demonstrate how variance reduction and measurement errors can be intrinsic to the method. Illustrative examples are presented, highlighting the practical challenges and possible solutions.
自1999年以来，最佳预测法（BP）在美国一直被用于根据已知的测定日产奶量来估算未观测到的每日和泌乳期产奶量。该方法已被证明比其前身更为准确。然而，在实际应用中它面临两个显著挑战。首先，与实际产奶量相比，BP降低了估计产奶量的方差。表型方差的降低令人担忧，因为它可能会严重低估遗传评估中的遗传变异。其次，不完整或不准确的测定日记录所预测的泌乳期产奶量会出现测量误差。这些误差会对泌乳期产奶量估计的准确性以及后续的遗传评估产生不利影响。本文对BP进行了分析性综述，重点关注方差降低和测量误差。我们展示了方差降低和测量误差如何可能是该方法固有的。文中给出了示例，突出了实际挑战和可能的解决方案。
